How to Extract, Transform and Load Data from Multiple Sources into Your CRM
ETL, which stands for extract, transform, and load, is a process for integrating data from multiple sources into a single management system. With Insycle, you can build a fully custom ETL solution.
Insycle offers a wide range of tools to help you filter and extract, transform, and load the data into or from your CRM. The various pre-built templates allow you to tackle common data extraction, transformation, and loading tasks with little configuration. You can also build your own custom templates for tackling ETL issues that are unique to your organization.
For some tasks, you can schedule automation so that specific ETL tasks—such as formatting or standardizing data, for example—are performed automatically on a set schedule.
With Insycle you can do things like:
- Format names, phone numbers, and addresses
- Standardize job titles, industries, locations
- Extract data from fields using flexible rules
- Remove invalid data and typos from fields
- Merge fields or move data between them
Key Use Cases
With Insycle, there are several ways you can standardize field data in your CRM. To analyze the variations in your database and make quick bulk changes, use the Cleanse Data module. The Transform Data module is perfect for standardizing data in bulk, one-time, or automatically on an ongoing basis. If you have data from an external source that you want to make consistent with your CRM while importing, use the Magical Import module.
To learn more, see:
When your data comes from many sources—website forms, internal data entry, integrations, and APIs—enforcing formats in specific fields is difficult. The inconsistent values make records difficult to segment and search for.
With Insycle, you can format any field in your database using pre-defined rulesets, and create templates to automate consistent field formatting. Existing data in any field can be formatted in bulk with the Transform Data module, or if you have an export from a third-party application or other data in a CSV, the new data can be formatted on import using the Magical Import module.
To learn more, see:
Ensuring that you have the correct data in the right field is important for keeping your data filterable and usable—both for your teams and for integrations. Doing this manually is a time-consuming process.
For example, on contact records that have a value in the Mobile Phone Number field, but don't have a value in the Phone Number field, you'd like to move data from the Mobile Phone Number to the Phone Number field.
In the Transform Data module, you can use rule-based templates to quickly identify fields and automatically copy or move data between them.
To learn more, see:
Ensuring that you have the correct data in the right field is important for keeping your data filterable and usable—both for your teams and for integrations. But sometimes, you need to fill in a field based on the values in another field. For example, when a contact's Job Title is "Chief Executive Officer," you may want to write "CEO Helena" into your Persona field.
The Transform Data module lets you filter data, and use existing data in a field to specify what value should be copied to a new field.
To learn more, see:
You need to make bulk updates to a field based on values that have been entered in another field. For example, when the State/Region value in a record is "Quebec," you want to set the Country field value to "Canada."
Insycle makes it easy to identify records that have a specific field value and update them based on your defined conditions. For straightforward, "If value=x, set it to=y," tasks, use the Bulk Operations module. For more complex tasks where you need to do multiple conditional bulk updates all in one template, the Transform Data module would be more appropriate.
To learn more, see the Conditional Bulk Update if Value=x Then Set It To=y article.
Additional Resources
Related Help Articles
- Module Overview: Transform Data
- Fix Data Inconsistencies
- Email and Phone Validation
- Break an Address Into Multiple Fields
- Use Regular Expressions (Regex) For Advanced Data Filtering and Manipulation
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